Heart Disease Prediction Using a Hybrid Random Forest–Artificial Neural Network Model: A Replication and Extension of the HRFLM Approach

The leading cause of death worldwide is cardiovascular disease, which is motivating screening using non-invasive machine learning on clinical parameters collected routinely. The present work paper reviews eight studies representative and heart disease prediction most relevant, highlights the major research gaps they leave open, and replicates a well-known hybrid baseline the Hybrid Random Forest with Linear Model (HRFLM) proposed by Mohan et al., and finally presents an improved Hybrid RF-ANN. The proposed model rescaling clinical input feature by their importance score derived from Random-Forest and feeding those features to two hidden layers NN. The Hybrid RF-ANN, whose code is published, attained an accuracy of 90.16% in the study, a significant 9.83 percentage point improvement over the accuracy of HRFLM, while the Hybrid RF-ANN achieved the highest ROC-AUC of all ten studied models (0.977), which is an enhancement of 0.0787 in ROC-AUC against HRFLM.